{"id":"W4250224963","doi":"10.3886/icpsr27901","title":"Assessing Happiness and Competitiveness of World Major Metropolises, 2006","year":2006,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"Regional resilience and development","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Happiness; Regional science; Economic geography; Geography; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006903553,0.0003520913,0.0009453082,0.0008181939,0.0001260524,0.0003295688,0.00124337,0.0001850139,0.0003750027],"category_scores_gemma":[0.0001827822,0.0003895891,0.00006883738,0.0005694043,0.0003037085,0.0009512088,0.0008742469,0.000268113,0.000129053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054646,"about_ca_system_score_gemma":0.0001018546,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008686871,"about_ca_topic_score_gemma":0.0006835158,"domain_scores_codex":[0.997529,0.00001763634,0.0009601036,0.0009676819,0.0001466065,0.0003789532],"domain_scores_gemma":[0.9973766,0.0001517685,0.000979347,0.001328354,0.00006359612,0.0001003266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008007782,0.00008230304,0.006591901,0.0002938304,0.00006178032,0.00001750748,0.000003740709,0.000002895158,0.000002854897,0.006425203,0.9862499,0.0002601081],"study_design_scores_gemma":[0.0003272137,0.00001215973,0.02512638,0.0002457352,0.00002814033,0.000008052041,0.00002163984,0.00002874974,0.00002398058,0.001606832,0.9721416,0.0004295482],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001235176,0.006246963,0.0002679024,0.0001382974,0.0003964107,0.0001926602,0.9895319,0.0000147542,0.001975892],"genre_scores_gemma":[0.001715383,0.001327152,0.001096965,0.0001961291,0.0001942468,0.00001623483,0.9945722,0.00002899132,0.0008527128],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01853448,"threshold_uncertainty_score":0.9998556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08878062010067794,"score_gpt":0.3006378604308353,"score_spread":0.2118572403301573,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}